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      <li>部署Single Shot Multibox Detector(SSD)模型</li>
    
    
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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">注解</p>
<p>点击 <a class="reference internal" href="#sphx-glr-download-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">这里</span></a> 下载完整的样例代码</p>
</div>
<div class="sphx-glr-example-title section" id="deploy-single-shot-multibox-detector-ssd-model">
<span id="sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"></span><h1>部署Single Shot Multibox Detector(SSD)模型<a class="headerlink" href="#deploy-single-shot-multibox-detector-ssd-model" title="永久链接至标题">¶</a></h1>
<p><strong>作者</strong>: <a class="reference external" href="https://github.com/kevinthesun">Yao Wang</a> <a class="reference external" href="https://github.com/Laurawly">Leyuan Wang</a></p>
<p>本文是使用TVM部署SSD模型的入门教程。我们将使用GluonCV预先训练的SSD模型，并将其转换为Relay IR</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tvm</span>
<span class="kn">from</span> <span class="nn">tvm</span> <span class="k">import</span> <span class="n">te</span>

<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">tvm</span> <span class="k">import</span> <span class="n">relay</span>
<span class="kn">from</span> <span class="nn">tvm.contrib</span> <span class="k">import</span> <span class="n">graph_executor</span>
<span class="kn">from</span> <span class="nn">tvm.contrib.download</span> <span class="k">import</span> <span class="n">download_testdata</span>
<span class="kn">from</span> <span class="nn">gluoncv</span> <span class="k">import</span> <span class="n">model_zoo</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">utils</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">输出:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/usr/local/lib/python3.6/dist-packages/gluoncv/__init__.py:40: UserWarning: Both `mxnet==1.6.0` and `torch==1.7.0` are installed. You might encounter increased GPU memory footprint if both framework are used at the same time.
  warnings.warn(f&#39;Both `mxnet=={mx.__version__}` and `torch=={torch.__version__}` are installed. &#39;
</pre></div>
</div>
<div class="section" id="preliminary-and-set-parameters">
<h2>初步设定参数<a class="headerlink" href="#preliminary-and-set-parameters" title="永久链接至标题">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">注解</p>
<p>我们现在支持在CPU和GPU上编译 SSD。</p>
<p>要在CPU上获得最佳推理性能，请根据您的设备更改目标参数，并按照:ref:<cite>tune_relay_x86</cite> to tune x86 CPU and :ref:<a href="#id1"><span class="problematic" id="id2">`</span></a>tune_relay_arm`调整arm CPU。</p>
<p>要在Intel graphics上获得最佳推理性能，请将目标参数更改为:code:<cite>opencl -device=intel_graphics</cite>。但在Mac上使用Intel graphics时，target需要设置为`opencl`，这只是因为Mac上不支持Intel subgroup extension。</p>
<p>要在基于CUDA的GPU上获得最佳推理性能，请将目标参数更改为 <code class="code docutils literal notranslate"><span class="pre">cuda</span></code>；对于基于OPENCL的GPU，根据您的设备，将目标参数更改为:code:<a href="#id1"><span class="problematic" id="id2">`</span></a>opencl`后跟设备参数。</p>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">supported_model</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s2">&quot;ssd_512_resnet50_v1_voc&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ssd_512_resnet50_v1_coco&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ssd_512_resnet101_v2_voc&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ssd_512_mobilenet1.0_voc&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ssd_512_mobilenet1.0_coco&quot;</span><span class="p">,</span>
    <span class="s2">&quot;ssd_300_vgg16_atrous_voc&quot;</span> <span class="s2">&quot;ssd_512_vgg16_atrous_coco&quot;</span><span class="p">,</span>
<span class="p">]</span>

<span class="n">model_name</span> <span class="o">=</span> <span class="n">supported_model</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">dshape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
</pre></div>
</div>
<p>下载并预处理演示图像</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">im_fname</span> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span>
    <span class="s2">&quot;https://github.com/dmlc/web-data/blob/main/&quot;</span> <span class="o">+</span> <span class="s2">&quot;gluoncv/detection/street_small.jpg?raw=true&quot;</span><span class="p">,</span>
    <span class="s2">&quot;street_small.jpg&quot;</span><span class="p">,</span>
    <span class="n">module</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">x</span><span class="p">,</span> <span class="n">img</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">presets</span><span class="o">.</span><span class="n">ssd</span><span class="o">.</span><span class="n">load_test</span><span class="p">(</span><span class="n">im_fname</span><span class="p">,</span> <span class="n">short</span><span class="o">=</span><span class="mi">512</span><span class="p">)</span>
</pre></div>
</div>
<p>转换和编译CPU模型。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">block</span> <span class="o">=</span> <span class="n">model_zoo</span><span class="o">.</span><span class="n">get_model</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="n">target</span><span class="p">):</span>
    <span class="n">mod</span><span class="p">,</span> <span class="n">params</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">frontend</span><span class="o">.</span><span class="n">from_mxnet</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;data&quot;</span><span class="p">:</span> <span class="n">dshape</span><span class="p">})</span>
    <span class="k">with</span> <span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
        <span class="n">lib</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">lib</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">输出:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/usr/local/lib/python3.6/dist-packages/mxnet/gluon/block.py:1389: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
        data: None
  input_sym_arg_type = in_param.infer_type()[0]
</pre></div>
</div>
<p>创建TVM运行时间并做推论.. 注意:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Use</span> <span class="n">target</span> <span class="o">=</span> <span class="s2">&quot;cuda -libs&quot;</span> <span class="n">to</span> <span class="n">enable</span> <span class="n">thrust</span> <span class="n">based</span> <span class="n">sort</span><span class="p">,</span> <span class="k">if</span> <span class="n">you</span>
<span class="n">enabled</span> <span class="n">thrust</span> <span class="n">during</span> <span class="n">cmake</span> <span class="n">by</span> <span class="o">-</span><span class="n">DUSE_THRUST</span><span class="o">=</span><span class="n">ON</span><span class="o">.</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="n">lib</span><span class="p">,</span> <span class="n">dev</span><span class="p">):</span>
    <span class="c1"># Build TVM runtime</span>
    <span class="n">m</span> <span class="o">=</span> <span class="n">graph_executor</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">(</span><span class="n">lib</span><span class="p">[</span><span class="s2">&quot;default&quot;</span><span class="p">](</span><span class="n">dev</span><span class="p">))</span>
    <span class="n">tvm_input</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">(),</span> <span class="n">device</span><span class="o">=</span><span class="n">dev</span><span class="p">)</span>
    <span class="n">m</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="n">tvm_input</span><span class="p">)</span>
    <span class="c1"># execute</span>
    <span class="n">m</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
    <span class="c1"># get outputs</span>
    <span class="n">class_IDs</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">bounding_boxs</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">m</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">m</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">class_IDs</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">bounding_boxs</span>


<span class="k">for</span> <span class="n">target</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;llvm&quot;</span><span class="p">,</span> <span class="s2">&quot;cuda&quot;</span><span class="p">]:</span>
    <span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">dev</span><span class="o">.</span><span class="n">exist</span><span class="p">:</span>
        <span class="n">lib</span> <span class="o">=</span> <span class="n">build</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
        <span class="n">class_IDs</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">bounding_boxs</span> <span class="o">=</span> <span class="n">run</span><span class="p">(</span><span class="n">lib</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span>
</pre></div>
</div>
<p>显示结果</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">viz</span><span class="o">.</span><span class="n">plot_bbox</span><span class="p">(</span>
    <span class="n">img</span><span class="p">,</span>
    <span class="n">bounding_boxs</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span>
    <span class="n">scores</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span>
    <span class="n">class_IDs</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span>
    <span class="n">class_names</span><span class="o">=</span><span class="n">block</span><span class="o">.</span><span class="n">classes</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
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<p class="sphx-glr-timing"><strong>脚本总运行时间:</strong> ( 2 minutes  31.833 seconds)</p>
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